{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Autoregressive Moving Average (ARMA): Artificial data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "import pandas as pd\n",
    "from statsmodels.tsa.arima_process import arma_generate_sample\n",
    "np.random.seed(12345)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate some data from an ARMA process:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "arparams = np.array([.75, -.25])\n",
    "maparams = np.array([.65, .35])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The conventions of the arma_generate function require that we specify a 1 for the zero-lag of the AR and MA parameters and that the AR parameters be negated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "arparams = np.r_[1, -arparams]\n",
    "maparams = np.r_[1, maparams]\n",
    "nobs = 250\n",
    "y = arma_generate_sample(arparams, maparams, nobs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Now, optionally, we can add some dates information. For this example, we'll use a pandas time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency M will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    }
   ],
   "source": [
    "dates = sm.tsa.datetools.dates_from_range('1980m1', length=nobs)\n",
    "y = pd.Series(y, index=dates)\n",
    "arma_mod = sm.tsa.ARMA(y, order=(2,2))\n",
    "arma_res = arma_mod.fit(trend='nc', disp=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              ARMA Model Results                              \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:                  250\n",
      "Model:                     ARMA(2, 2)   Log Likelihood                -353.445\n",
      "Method:                       css-mle   S.D. of innovations              0.990\n",
      "Date:                Tue, 24 Dec 2019   AIC                            716.891\n",
      "Time:                        15:06:16   BIC                            734.498\n",
      "Sample:                    01-31-1980   HQIC                           723.977\n",
      "                         - 10-31-2000                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1.y        0.7904      0.134      5.878      0.000       0.527       1.054\n",
      "ar.L2.y       -0.2314      0.113     -2.044      0.042      -0.453      -0.009\n",
      "ma.L1.y        0.7007      0.127      5.525      0.000       0.452       0.949\n",
      "ma.L2.y        0.4061      0.095      4.291      0.000       0.221       0.592\n",
      "                                    Roots                                    \n",
      "=============================================================================\n",
      "                  Real          Imaginary           Modulus         Frequency\n",
      "-----------------------------------------------------------------------------\n",
      "AR.1            1.7079           -1.1851j            2.0788           -0.0965\n",
      "AR.2            1.7079           +1.1851j            2.0788            0.0965\n",
      "MA.1           -0.8628           -1.3108j            1.5693           -0.3427\n",
      "MA.2           -0.8628           +1.3108j            1.5693            0.3427\n",
      "-----------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "print(arma_res.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-06-30    0.173211\n",
       "2000-07-31   -0.048325\n",
       "2000-08-31   -0.415804\n",
       "2000-09-30    0.338725\n",
       "2000-10-31    0.360838\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "fig = arma_res.plot_predict(start='1999-06-30', end='2001-05-31', ax=ax)\n",
    "legend = ax.legend(loc='upper left')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
